Patient specific modeling is used in connection with surgical and orthopedic procedures to plan a surgery or to design instruments and implants for the surgery. A patient specific model allows a surgeon to account for variation in anatomy between patients by first modeling the portion of the body at which the surgery is carried out. The surgical procedure can be precisely planned by tailoring the approach and instruments to particular variations in the patient's anatomy that may otherwise cause difficulties during a standard procedure. As a first step in the process, the patient's anatomy is imaged by standard medical technology, for example using an MRI or a CT scanning machine, in order to obtain a data set that is representative of the patient's anatomy. The data set that is obtained indicates any particular variations or nuances in the patient's anatomy, and processing that data can provide a surgeon with a detailed map of the relevant body portion ahead of time.
The imaging data obtained from the patient's anatomy is processed to create a model of the patient's anatomy that is used to plan the procedure. The raw data set can be processed in a number of different ways, including filtering, interpolation, sampling, and other data processing procedures that turn the data into a digital anatomy model for the surgeon's use. One particular processing approach is image segmentation, in which the full data set is analyzed in blocks, with each block representing a different area of the relevant anatomy. These processing techniques, including segmentation, can introduce errors into the model as a result of the estimation that compresses and otherwise processes the data. For example, there may be rounding or smoothing effects that create a smooth surface in the model that does not account for the deviations from that smooth surface that are actually present in the patient's anatomy. For some procedures, these processing errors are of particular interest when they occur at boundary regions between different types of tissue in the model. At these boundary regions, the data processing algorithm may not produce a model in which the division between two tissues is precisely estimated. Additionally, processing errors may be of interest when they occur at an anatomical landmark such as the medial condyle, lateral condyle, or Whiteside's line. At anatomical landmarks, the data processing algorithm may incorrectly identify a landmark location due to errors in the model.
The amount of processing performed on a raw data set of imaging data is correlated with the number of potential estimation errors introduced to a patient model. The more times or ways in which the data is sampled, smoothed, or estimated, the greater the chance will be that there are errors and deviations introduced into the model that do not completely reflect the patient's anatomy with precision. When these estimations and deviations occur in areas of the anatomy that are important for the surgical procedure being planned, they may lead to complications during the surgery. For example, a surgical guide or implant that does not correctly match the patient's bone may result in longer surgeries due to poorly fitted instrumentation or an abandonment of the surgery altogether.
For some procedures, patient implants and instrumentation such as surgical guides are designed to match a specific patient's bone anatomy. In these cases, accurate models can be helpful to create an implant or surgical guide that will closely interface with the patient's bone. Any deviations or variations between the model and the actual anatomy, particularly in areas where the implant or surgical guide interfaces with the bone, may reduce the effectiveness of the surgical procedure. For such applications, it would be helpful to have an indication not only of the patient's estimated anatomy, but also an indication of how closely the modeled anatomy maps to the real anatomy. Providing a surgeon or other operator with indications of accuracy directly on an anatomy model would be beneficial and could lead to early error detection and improvement of surgical procedures.